A multiple regression normalization approach to evaluation of gait in total knee arthroplasty patients
Gait features characteristic of a cohort may be difficult to evaluate due to differences in subjects' demographic factors and walking speed. The aim of this study was to employ a multiple regression normalization method that accounts for subject age, height, body mass, gender, and self-selected...
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| Published in | Clinical biomechanics (Bristol) Vol. 32; no. NA; pp. 92 - 101 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
England
Elsevier Ltd
01.02.2016
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0268-0033 1879-1271 1879-1271 |
| DOI | 10.1016/j.clinbiomech.2015.12.012 |
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| Abstract | Gait features characteristic of a cohort may be difficult to evaluate due to differences in subjects' demographic factors and walking speed. The aim of this study was to employ a multiple regression normalization method that accounts for subject age, height, body mass, gender, and self-selected walking speed in the evaluation of gait in unilateral total knee arthroplasty patients.
Three-dimensional gait analysis was performed on 45 total knee arthroplasty patients and 31 aged-matched controls walking at their self-selected speed. Gait data peaks including joint angles, ground reaction forces, net joint moments, and net joint powers were normalized using subject body mass, standard dimensionless equations, and a multiple regression approach that modeled subject age, height, body mass, gender, and self-selected walking speed.
Normalizing gait data using subject body mass, dimensionless equations, and multiple regression approach resulted in a significantly lower knee adduction moment and knee extensor power in total knee arthroplasty patients compared to controls (p<0.05). In contrast to normalization using body mass and dimensionless equations, multiple regression normalization greatly reduced variance in gait data by minimizing correlations with subject demographic factors and walking speed, resulting in significantly higher peak hip extension angles and peak hip flexion powers in total knee arthroplasty patients (p<0.05).
Total knee arthroplasty patients generate greater hip extension angles and hip flexor power and have a lower knee adduction moment than healthy controls. This gait pattern may be a strategy to reduce muscle and joint loading at the knee.
•Variance in gait data may limit capacity to discern gait patterns in a cohort.•Multiple-regression normalization decorrelates demographic factors from gait data•TKA patients have reduced knee function but employ greater hip power |
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| AbstractList | Gait features characteristic of a cohort may be difficult to evaluate due to differences in subjects' demographic factors and walking speed. The aim of this study was to employ a multiple regression normalization method that accounts for subject age, height, body mass, gender, and self-selected walking speed in the evaluation of gait in unilateral total knee arthroplasty patients.
Three-dimensional gait analysis was performed on 45 total knee arthroplasty patients and 31 aged-matched controls walking at their self-selected speed. Gait data peaks including joint angles, ground reaction forces, net joint moments, and net joint powers were normalized using subject body mass, standard dimensionless equations, and a multiple regression approach that modeled subject age, height, body mass, gender, and self-selected walking speed.
Normalizing gait data using subject body mass, dimensionless equations, and multiple regression approach resulted in a significantly lower knee adduction moment and knee extensor power in total knee arthroplasty patients compared to controls (p<0.05). In contrast to normalization using body mass and dimensionless equations, multiple regression normalization greatly reduced variance in gait data by minimizing correlations with subject demographic factors and walking speed, resulting in significantly higher peak hip extension angles and peak hip flexion powers in total knee arthroplasty patients (p<0.05).
Total knee arthroplasty patients generate greater hip extension angles and hip flexor power and have a lower knee adduction moment than healthy controls. This gait pattern may be a strategy to reduce muscle and joint loading at the knee. Gait features characteristic of a cohort may be difficult to evaluate due to differences in subjects' demographic factors and walking speed. The aim of this study was to employ a multiple regression normalization method that accounts for subject age, height, body mass, gender, and self-selected walking speed in the evaluation of gait in unilateral total knee arthroplasty patients. Three-dimensional gait analysis was performed on 45 total knee arthroplasty patients and 31 aged-matched controls walking at their self-selected speed. Gait data peaks including joint angles, ground reaction forces, net joint moments, and net joint powers were normalized using subject body mass, standard dimensionless equations, and a multiple regression approach that modeled subject age, height, body mass, gender, and self-selected walking speed. Normalizing gait data using subject body mass, dimensionless equations, and multiple regression approach resulted in a significantly lower knee adduction moment and knee extensor power in total knee arthroplasty patients compared to controls (p<0.05). In contrast to normalization using body mass and dimensionless equations, multiple regression normalization greatly reduced variance in gait data by minimizing correlations with subject demographic factors and walking speed, resulting in significantly higher peak hip extension angles and peak hip flexion powers in total knee arthroplasty patients (p<0.05). Total knee arthroplasty patients generate greater hip extension angles and hip flexor power and have a lower knee adduction moment than healthy controls. This gait pattern may be a strategy to reduce muscle and joint loading at the knee. •Variance in gait data may limit capacity to discern gait patterns in a cohort.•Multiple-regression normalization decorrelates demographic factors from gait data•TKA patients have reduced knee function but employ greater hip power Background: Gait features characteristic of a cohort may be difficult to evaluate due to differences in subjects' demographic factors and walking speed. The aim of this study was to employ a multiple regression normalization method that accounts for subject age, height, body mass, gender, and self-selected walking speed in the evaluation of gait in unilateral total knee arthroplasty patients. Methods: Three-dimensional gait analysis was performed on 45 total knee arthroplasty patients and 31 aged-matched controls walking at their self-selected speed. Gait data peaks including joint angles, ground reaction forces, net joint moments, and net joint powers were normalized using subject body mass, standard dimensionless equations, and a multiple regression approach that modeled subject age, height, body mass, gender, and self-selected walking speed. Findings: Normalizing gait data using subject body mass, dimensionless equations, and multiple regression approach resulted in a significantly lower knee adduction moment and knee extensor power in total knee arthroplasty patients compared to controls (p < 0.05). In contrast to normalization using body mass and dimensionless equations, multiple regression normalization greatly reduced variance in gait data by minimizing correlations with subject demographic factors and walking speed, resulting in significantly higher peak hip extension angles and peak hip flexion powers in total knee arthroplasty patients (p < 0.05). Interpretation: Total knee arthroplasty patients generate greater hip extension angles and hip flexor power and have a lower knee adduction moment than healthy controls. This gait pattern may be a strategy to reduce muscle and joint loading at the knee. Abstract Background Gait features characteristic of a cohort may be difficult to evaluate due to differences in subjects’ demographic factors and walking speed. The aim of this study was to employ a multiple regression normalization method that accounts for subject age, height, body mass, gender, and self-selected walking speed in the evaluation of gait of unilateral total knee arthroplasty patients. Methods Three-dimensional gait analysis was performed on 45 total knee arthroplasty patients and 31 aged-matched controls walking at their self-selected speed. Gait data peaks including joint angles, ground reaction forces, net joint moments, and net joint powers were normalized using subject body mass, standard dimensionless equations, and a multiple regression approach that modelled subject age, height, body mass, gender, and self-selected walking speed. Findings Normalizing gait data using subject body mass, dimensionless equations, and multiple regression approach resulted in a significantly lower knee adduction moment and knee extensor power in total knee arthroplasty patients compared to controls (p < 0.05). In contrast to normalization using body mass and dimensionless equations, multiple regression normalization greatly reduced variance in gait data by minimizing correlations with subject demographic factors and walking speed, resulting in significantly higher peak hip extension angles and peak hip flexion powers in total knee arthroplasty patients (p < 0.05). Interpretation Total knee arthroplasty patients generate greater hip extension angles and hip flexor power, and have a lower knee adduction moment than healthy controls. This gait pattern may be a strategy to reduce muscle and joint loading at the knee. Gait features characteristic of a cohort may be difficult to evaluate due to differences in subjects' demographic factors and walking speed. The aim of this study was to employ a multiple regression normalization method that accounts for subject age, height, body mass, gender, and self-selected walking speed in the evaluation of gait in unilateral total knee arthroplasty patients.BACKGROUNDGait features characteristic of a cohort may be difficult to evaluate due to differences in subjects' demographic factors and walking speed. The aim of this study was to employ a multiple regression normalization method that accounts for subject age, height, body mass, gender, and self-selected walking speed in the evaluation of gait in unilateral total knee arthroplasty patients.Three-dimensional gait analysis was performed on 45 total knee arthroplasty patients and 31 aged-matched controls walking at their self-selected speed. Gait data peaks including joint angles, ground reaction forces, net joint moments, and net joint powers were normalized using subject body mass, standard dimensionless equations, and a multiple regression approach that modeled subject age, height, body mass, gender, and self-selected walking speed.METHODSThree-dimensional gait analysis was performed on 45 total knee arthroplasty patients and 31 aged-matched controls walking at their self-selected speed. Gait data peaks including joint angles, ground reaction forces, net joint moments, and net joint powers were normalized using subject body mass, standard dimensionless equations, and a multiple regression approach that modeled subject age, height, body mass, gender, and self-selected walking speed.Normalizing gait data using subject body mass, dimensionless equations, and multiple regression approach resulted in a significantly lower knee adduction moment and knee extensor power in total knee arthroplasty patients compared to controls (p<0.05). In contrast to normalization using body mass and dimensionless equations, multiple regression normalization greatly reduced variance in gait data by minimizing correlations with subject demographic factors and walking speed, resulting in significantly higher peak hip extension angles and peak hip flexion powers in total knee arthroplasty patients (p<0.05).FINDINGSNormalizing gait data using subject body mass, dimensionless equations, and multiple regression approach resulted in a significantly lower knee adduction moment and knee extensor power in total knee arthroplasty patients compared to controls (p<0.05). In contrast to normalization using body mass and dimensionless equations, multiple regression normalization greatly reduced variance in gait data by minimizing correlations with subject demographic factors and walking speed, resulting in significantly higher peak hip extension angles and peak hip flexion powers in total knee arthroplasty patients (p<0.05).Total knee arthroplasty patients generate greater hip extension angles and hip flexor power and have a lower knee adduction moment than healthy controls. This gait pattern may be a strategy to reduce muscle and joint loading at the knee.INTERPRETATIONTotal knee arthroplasty patients generate greater hip extension angles and hip flexor power and have a lower knee adduction moment than healthy controls. This gait pattern may be a strategy to reduce muscle and joint loading at the knee. |
| Author | Ackland, David C. Webster, Kate E. Halgamuge, Saman Begg, Rezaul McClelland, Jodie A. Wahid, Ferdous |
| Author_xml | – sequence: 1 givenname: Ferdous surname: Wahid fullname: Wahid, Ferdous organization: Department of Mechanical Engineering, University of Melbourne, Melbourne, VIC, Australia – sequence: 2 givenname: Rezaul surname: Begg fullname: Begg, Rezaul organization: College of Sport and Exercise Science, VIC University, Melbourne, VIC, Australia – sequence: 3 givenname: Jodie A. surname: McClelland fullname: McClelland, Jodie A. organization: School of Allied Health, La Trobe University, Melbourne, VIC, Australia – sequence: 4 givenname: Kate E. surname: Webster fullname: Webster, Kate E. organization: School of Allied Health, La Trobe University, Melbourne, VIC, Australia – sequence: 5 givenname: Saman surname: Halgamuge fullname: Halgamuge, Saman organization: Department of Mechanical Engineering, University of Melbourne, Melbourne, VIC, Australia – sequence: 6 givenname: David C. surname: Ackland fullname: Ackland, David C. email: dackland@unimelb.edu.au organization: Department of Mechanical Engineering, University of Melbourne, Melbourne, VIC, Australia |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/26874198$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1080_14763141_2023_2205378 crossref_primary_10_1016_j_clinbiomech_2024_106267 crossref_primary_10_1016_j_clinbiomech_2019_05_004 crossref_primary_10_1007_s40846_020_00533_8 crossref_primary_10_1016_j_arth_2017_09_031 crossref_primary_10_1371_journal_pone_0160490 |
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| Keywords | Biomechanics Walking Joint replacement Regression model regression model biomechanics walking joint replacement |
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| Snippet | Gait features characteristic of a cohort may be difficult to evaluate due to differences in subjects' demographic factors and walking speed. The aim of this... Abstract Background Gait features characteristic of a cohort may be difficult to evaluate due to differences in subjects’ demographic factors and walking... Background: Gait features characteristic of a cohort may be difficult to evaluate due to differences in subjects' demographic factors and walking speed. The... |
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| SubjectTerms | Adult Aged Aged, 80 and over Arthroplasty, Replacement, Knee - methods Arthroplasty, Replacement, Knee - rehabilitation Biomechanics Biomedical materials Cohort Studies Female Gait Gait - physiology Humans Imaging, Three-Dimensional Joint replacement Knee Joint - physiology Knees Male Mathematical models Middle Aged Patients Physical Medicine and Rehabilitation Range of Motion, Articular - physiology Regression Regression Analysis Regression model Stress, Mechanical Surgical implants Walking Walking - physiology |
| Title | A multiple regression normalization approach to evaluation of gait in total knee arthroplasty patients |
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